Window Dressing in Reported Earnings

The article discusses the use of the term window dressing, a wide range of techniques for auditing, by audit clients to enhance the financial position of an entity through manipulated disclosures. The term refers to the reporting practices adopted by firms to distort earnings by changing the way stakeholders perceived the financial figures. A research suggests that firms must engage in the type of manipulative behavior for the purpose of economic incentives

Earnings Management and Long-Run Stock Underperformance of Private Placements

The study investigates whether private placement issuers manipulate their earnings around the time of issuance and the effect of earnings management on the long-run stock performance. We find that managers of U.S. private placement issuers tend to engage in income-increasing earnings management in the year prior to the issuance of private placements. We further speculate that earnings management serves as a likely source of investor over-optimism at the time of private placements. To support this speculation, we find evidence suggesting that the income-increasing accounting accruals made at the time of private placements predict the post-issue long-term stock underperformance. The study contributes to the large body of literature on earnings manipulation around the time of securities issuance

Feature selection for modular GA-based classification

Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. Feature selection plays an important role in finding relevant features in classification. In this paper, feature selection is explored with modular GA-based classification. A new feature selection technique, Relative Importance Factor (RIF), is proposed to find less relevant features in the input domain of each class module. By removing these features, it is aimed to reduce the classification error and dimensionality of classification problems. Benchmark classification data sets are used to evaluate the proposed approach. The experiment results show that RIF can be used to find less relevant features and help achieve lower classification error with the feature space dimension reduced

Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring the susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g. in vivo mouse brain data and brains with lesions, which suggests that the network has generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction and high reconstruction speed demonstrate its potential for future applications.Comment: 26 page

Correction to: Knowledge Graph and Deep Learning-based Text-to-GraphQL Model for Intelligent Medical Consultation Chatbot

Correction to: Information Systems Frontiers (https://doi.org/10.1007/s10796-022-10295-0)

Fourier Neural Operator Networks: A Fast and General Solver for the Photoacoustic Wave Equation

Simulation tools for photoacoustic wave propagation have played a key role in advancing photoacoustic imaging by providing quantitative and qualitative insights into parameters affecting image quality. Classical methods for numerically solving the photoacoustic wave equation relies on a fine discretization of space and can become computationally expensive for large computational grids. In this work, we apply Fourier Neural Operator (FNO) networks as a fast data-driven deep learning method for solving the 2D photoacoustic wave equation in a homogeneous medium. Comparisons between the FNO network and pseudo-spectral time domain approach demonstrated that the FNO network generated comparable simulations with small errors and was several orders of magnitude faster. Moreover, the FNO network was generalizable and can generate simulations not observed in the training data

HandyBroker - An intelligent product-brokering agent for M-commerce applications with user preference tracking

One of the potential applications for agent-based systems is m-commerce. A lot of research has been done on making such systems intelligent to personalize their services for users. In most systems, user-supplied keywords are generally used to help generate profiles for users. In this paper, an evolutionary ontology-based product-brokering agent has been designed for m-commerce applications. It uses an evaluation function to represent a user’s preference instead of the usual keyword-based profile. By using genetic algorithms, the agent tracks the user’s preferences for a particular product by tuning some parameters inside its evaluation function. A prototype called “Handy Broker” has been implemented in Java and the results obtained from our experiments looks promising for m-commerce use

Manual to Automated Testing

Purpose - The purpose of this case study is to understand how two organizations view and utilise automated testing and how it relates to the literature. It outlines and discusses the key factors to be taken into consideration when setting up an automated testing, in addition to the risks involved. Design/methodology/approach – Focus group discussions were executed to collect the data and the findings were compared with other literatures. Findings – For cognition of automated testing, it is not only limited to its definition and benefits that may be brought into the organization, but also need to focus more on scope of application and preconditions. Aside from the key considerations such as people resistance, working process and training, some other concerns were also found from managerial perspective when adopting automated testing: (1) Cost-benefit – Return of Investment (ROI) is an effective method to analyse the investment, especially for the factors affecting the cost of investment; (2) Management support. It includes balancing between business and technology, management involvement and coordinating the relations between the departments; (3) Tool selection: Choosing the right automation tool is a very complicated process with a lot internal factors involved. Practical implication – For an organization that doesn’t have automated testing implemented yet, a pilot project can be the first option to understand its practicality and applicability based on individual organizational context. Originality/Value – This case study can be used for an organization that interests in better introducing and implementing automated testing within the organization. Key Words – Automated testing, Cost-benefit, Management support, Tool selection, practicality, applicability and ROI. Paper Type – Case Study Research

A Factory-based Approach to Support E-commerce Agent Fabrication

With the development of Internet computing and software agent technologies, agent-based e-commerce is emerging. How to create agents for e-commerce applications has become an important issue along the way to success. We propose a factory-based approach to support agent fabrication in e-commerce and elaborate a design based on the SAFER (Secure Agent Fabrication, Evolution & Roaming) framework. The details of agent fabrication, modular agent structure, agent life cycle, as well as advantages of agent fabrication are presented. Product-brokering agent is employed as a practical agent type to demonstrate our design and Java-based implementation